(A) Schematic microcircuit of the striatum. (B) Raster plot of MSNs spiking activity in a striatum network model dominated by FF inhibition. The FF inhibition induced synchrony, but not oscillations in the MSN population activity. (C) Low firing rate and asynchronous, irregular spiking activity in a striatum network in presence of both FF and FB inhibitions. (D) Mean firing rate of the MSNs for different feedforward (FF) and feedback (FB) inhibition strengths. (E) Synchrony index (cf. Methods) of the population activity for different FF and FB inhibition strengths.

(A) Spiking activity in the striatum for   = 0. Blue and black rasters show the spiking activity of MSNs and FSIs, respectively. PSTHs of the corresponding rasters are shown at the bottom. (B) Spiking activity in the striatum for   = 1. Red and black rasters show the spiking activity of MSNs and FSIs, respectively. PSTHs of the corresponding rasters are shown at the bottom. (C) Firing rate of the stimulated MSNs, averaged over the stimulation epoch, as a function of input correlation , for four different values of . (D) Firing rate of the unstimulated MSNs, averaged over the stimulation epoch, as a function of input correlation , for four different values of . (E) Signal-to-noise ratio (SNR) of the striatum network as a function of input correlation , for four different values of FSIs' output correlation . Observe that inhibitory input correlation decreased the SNR, which maintained its non-monotonicity and shifted its peak to a slightly lower value of .

(A) A schematic of two competing functional groups of MSNs. The green group of MSNs received stimulus input (firing rate , correlations ). The red group of MSNs received stimulus input at a rate of , while the within-pool correlation was varied systematically. (B) Spiking activity in the MSNs when both red and green group received stimulus inputs. In this example within-pool correaltion for the the red group is suboptimal (  = 0.005). The green group has a higher output rate even though it receives a smaller amount of stimulated input. (C) The output rates of the two functional groups of MSNs when for the red group was varied systematically. The dashed line shows the value used in the panel B. When within-pool correlation for the red group is smaller than 0.01, green group ‘wins’ even though it receives only half the input firing rate as compared to the red group, illustrating a function role that input correlation can play in the striatum.

(A) Example of the free membrane potential traces of two stimulated MSNs without any input correlation ( = 0,   = 0). As there was no correlation, neither within nor between the inputs to the two MSNs, the free membrane potentials did not show any significant correlation. (B) Example of the free membrane potential traces of two stimulated MSNs with moderate individual input correlation ( = 0.02) and full shared correlation (  = 1). Since here, the two MSNs were driven by correlated synaptic inputs, the free membrane potential traces showed significant correlation, together with substantial membrane potential fluctuations. (C) Cross correlation of the free membrane potentials of stimulated MSN pairs as a function of time lag for the two stimulus protocols. Strong correlation was observed at zero time lag for moderate individual input correlation ( = 0.02) and maximum shared input correlation (  = 1). (D) Average correlation coefficient at zero time lag of the free membrane potentials of stimulated MSN pairs as a function of shared input correlation , for different values of individual input correlation . The correlation coefficient increased monotonically with and did not show any dependence on . Note that the correlation coefficient had a small positive value for   = 0, because of the small synchrony effect arising from the FB and FF inhibitions. (E) Standard deviation of free membrane potential fluctuations for stimulated (solid lines) and unstimulated (dashed lines) MSNs as a function of individual input correlation at different values of . The free membrane potential fluctuation of the stimulated MSNs increased monotonically with , whereas that of the unstimulated MSNs did not show any significant change.

(A) Scheme of stimulus configuration-II (0; 0) presented to a fraction of striatum neurons, on top of the background excitatory input from the cortex and background inhibitory input from other striatal neurons. (B,C) Examples of MSNs spiking responses for two different stimuli to 30% of striatal neurons, with identical input firing rate ( = 400 Hz) and internal correlation ( = 0.02), but different shared correlations across stimulated neurons: low ( = 0.2; B) or high (  = 1.0; C), respectively. (D,E) Firing rate of the stimulated MSNs (D) and the unstimulated MSNs (E), averaged over the stimulation epoch, as a function of input correlation , for three different values of shared correlation . (F) Synchrony index of the stimulated MSNs (F) and the unstimulated MSNs (G) as a function of input correlation , for three different values of shared input correlation . Observe that the synchrony index of the stimulated MSNs increased both with increasing and increasing , due to the fact that larger led to more shared inputs among stimulated neurons, while larger resulted in more reliable spiking. In the unstimulated MSNs, (low values of did not influence synchrony, whereas high increased synchrony, due to the synchronized inhibition, induced by the synchronized spiking of the stimulated population. (H) Signal-to-noise ratio (SNR) of the striatum network as a function of both input correlation and shared correlation at  = 0.3. Observe that maximal SNR was obtained for weakly correlated () input to individual striatal neurons and uncorrelated (  = 0) inputs to different striatal neurons. (I) Signal-to-noise ratio (SNR) of the striatum network as a function of both input correlation and stimulated fraction at  = 0.2. Observe that maximal SNR was obtained for weakly correlated () input to a larger fraction of stimulated MSNs (larger ).

(A) Scheme of stimulus configuration-I ( = 0; 0) presented to a fraction of striatum neurons, on top of the background excitatory input from the cortex and background inhibitory input from other striatal neurons. (B,C) Examples of MSNs spiking responses when 30% of striatal neurons were stimulated for 100 ms (starting at 600 ms) with excitatory input with an ensemble firing rate  = 400 Hz and low ( = 0.001; B) or high (  = 0.02; C) input correlations, respectively. (D,E) Firing rate of the stimulated MSNs (D) and the unstimulated MSNs (E), averaged over the stimulation epoch, as a function of input correlation , for two different input firing rates. Observe that the response rate in both subpopulation varied in a non-monotonic fashion with increasing input correlation . The dashed line indicates the level of baseline activity. (F,G) Synchrony index of the stimulated MSNs (F) and the unstimulated MSNs (G) as a function of input correlation . The synchrony index of the stimulated MSNs is close to 1 so there is no significant synchrony. (H) Signal-to-noise ratio (SNR) of the striatum network, quantified by the ratio of the average firing rates of the stimulated and unstimulated MSNs, as a function of input correlation . Observe that SNR varied in a non-monotonic fashion with increasing input correlation . By contrast, SNR increased monotonically with input firing rate (). (I) Peak SNR of the striatum network as a function of input correlation for different strengths of feedback and feedforward inhibition . The blue trace shows peak SNR for different values of and a fixed  = 1 nS. The red trace shows peak SNR for different values of and a fixed   = 0.3 nS. Observe that increasing either type of inhibition increased the peak SNR, because stronger inhibition is more effective in suppressing the background activity.